Re-Sampling Based Data Mining Using Rough Set Theory

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Author(s): Benjamin Griffiths (Cardiff University, UK)
Copyright: 2007
Pages: 21
Source title: Knowledge Discovery and Data Mining: Challenges and Realities
Source Author(s)/Editor(s): Xingquan Zhu (University of Vermont, USA)and Ian Davidson (State University of New York at Albany, USA)
DOI: 10.4018/978-1-59904-252-7.ch013

Keywords: Data Mining and Databases / Information Science Reference / Knowledge Discovery / Library & Information Science

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Abstract

Predictive accuracy, as an estimation of a classifier’s future performance, has been studied for at least seventy years. With the advent of the modern computer era, techniques that may have been previously impractical are now calculable within a reasonable time frame. Within this chapter, three techniques of resampling, namely, leave-one-out, k-fold cross validation and bootstrapping; are investigated as methods of error rate estimation with application to variable precision rough set theory (VPRS). A prototype expert system is utilised to explore the nature of each resampling technique when VPRS is applied to an example dataset. The software produces a series of graphs and descriptive statistics, which are used to illustrate the characteristics of each technique with regards to VPRS, and comparisons are drawn between the results.

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Re-Sampling Based Data Mining Using Rough Set Theory

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